Theropod Teeth
Carolina Marques
2024-11-14
This document was compiled on the 2024-11-14 14:38:51.986189 by carol.
1 Introduction
The data (Database_Hendrickx_2019_Dentes Poyos.xlsx) was obtained thanks to Elisabete Malafaia (EM), on the 31/07/2024, via external memory to Carolina Marques (CM).
The data contains the information of several parameters obtained from measurements of theropod teeth and most of them are explained in the following schemes:
Fig 1: First scheme with the variabes obtained
from the theropods teeth.
Fig 2: Second scheme with the variabes obtained
from the theropods teeth, in real teeth.
Fig 3: Third scheme with the variabes obtained
from the theropods teeth, in real teeth.
All of the above schemes come from Hendrickx, Mateus, and Araújo (2015)
2 Reading the data
#data1 <- read_xlsx("Database_Hendrickx_2019_Dentes Poyos_Informacao idade.xlsx")
#dd<-data.table(Epoch=data1$Epoch,"Taxa (Genus)"=data1$`Taxa (Genus)`)
#dd<-dd[!duplicated(dd),]
data <- read_xlsx("Crown measurement dataset Kem Kem theropods.xlsx")
data[data == "?"] <- NA
data[data == "~"] <- NA
data[data == "/"] <- NA
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub("\\? ","", x)
})
data[] <- lapply(data, function(x) {
gsub("absent",0, x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub("\\?","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub("\\>","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub("\\<","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub(">","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub("<","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub("\\~","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub("~","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub(":","", x)
})
# Remove "?" from all columns
data[] <- lapply(data, function(x) {
gsub(";","", x)
})
data$LIF<- ifelse(data$LIF=="6-7",6.5,
ifelse(data$LIF=="5-6",5.5,
ifelse(data$LIF=="4-5",4.5,
ifelse(data$LIF=="3-4","3.5",
ifelse(data$LIF=="11 or 12",11.5,
ifelse(data$LIF=="10-13",12,data$LIF))))))
data$CH<-data$CH...22
data<- data %>% select(-CH...60,-CH...22,-`(DDL/CH)*100`)
data1<-data
data<-data[,-c(1,2,3,4,6:14,16:19)]#until 19
#clade: 4, taxa:2, teethtaxa: 3, cladetteth: 5, epoch:15
#data<-inner_join(dd,data)
#data<-data[!duplicated(data),]
data$`TransvUndu`<-ifelse(data$`Transv. Undu.`!=0 & !is.na(data$`Transv. Undu.`),1,data$`Transv. Undu.`)
data$`Interdentsulci`<-ifelse(data$`Interdent. sulci`!=0 & !is.na(data$`Interdent. sulci`),1,data$`Interdent. sulci`)
data$LAF<-ifelse(data$LAF=="6-7",6.5,data$LAF)
data$CTU1 <- sub(".*?(\\d+).*", "\\1", data$CTU)
data<- data %>% select(-CTU,-`Interdent. sulci`,-`Transv. Undu.`)
# Convert columns to numeric, then create log-transformed columns
data <- data %>%
mutate(across(3:ncol(data), as.numeric)) %>%
mutate(across(3:ncol(data), log, .names = "Log_{.col}"))
data$CladeToothtype<-as.factor(data$CladeToothtype)
data$Epoch<-ifelse(data$Epoch=="'Middle Cretaceous'","Middle Cretaceous", data$Epoch)
data$Epoch<-as.factor(data$Epoch)
# Columns to be checked
#columns_to_check <- c("MA", "MC", "MB", "DA", "DC", "DB", "MAVG", "DAVG", "DSDI")
# Replace values equal to 100 with 0 in the specified columns
#data[columns_to_check] <- lapply(data[columns_to_check], function(x) {
# x[x == 100] <- 0
#return(x)
#})
#data$Taxa<-as.factor(paste0(data$`Taxa (Genus)`,data$Maturity,sep=" "))
data<-data.frame(data)3 Checking the data
3.1 Summary of the table
## CladeToothtype Epoch
## Dromaeosauridae Lateral :317 Late Cretaceous :724
## Tyrannosauridae Lateral :185 Middle Cretaceous:238
## Carcharodontosauridae Lateral : 85 Late Jurassic :205
## Troodontidae Lateral : 81 Early Cretaceous : 81
## Abelisauridae Lateral : 73 Late Triassic : 57
## Non-spinosaurid Megalosauroidea Lateral: 72 Middle Jurassic : 55
## (Other) :558 (Other) : 11
## CBL CBW AL CBR
## Min. : 0.380 Min. : 0.540 Min. : 0.55 Min. :0.2500
## 1st Qu.: 4.282 1st Qu.: 2.300 1st Qu.: 12.40 1st Qu.:0.4598
## Median : 9.950 Median : 6.100 Median : 29.82 Median :0.5420
## Mean :13.916 Mean : 9.074 Mean : 37.19 Mean :0.5920
## 3rd Qu.:19.782 3rd Qu.:13.430 3rd Qu.: 55.15 3rd Qu.:0.6895
## Max. :54.500 Max. :48.600 Max. :152.84 Max. :2.1841
## NA's :3 NA's :69 NA's :338 NA's :81
## CHR MCL MCW MCR
## Min. :0.400 Min. : 0.32 Min. : 0.940 Min. :0.3841
## 1st Qu.:1.634 1st Qu.: 6.69 1st Qu.: 4.480 1st Qu.:0.5000
## Median :1.908 Median :12.13 Median : 7.390 Median :0.5745
## Mean :1.935 Mean :13.02 Mean : 8.274 Mean :0.6087
## 3rd Qu.:2.191 3rd Qu.:17.98 3rd Qu.:10.960 3rd Qu.:0.6818
## Max. :4.222 Max. :37.10 Max. :30.200 Max. :1.2792
## NA's :14 NA's :876 NA's :906 NA's :907
## MDE MSL MEC LAF
## Min. :-13.880 Min. : 1.42 Min. : 0.00 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 17.55 1st Qu.: 68.74 1st Qu.: 0.0000
## Median : 0.000 Median : 28.02 Median :100.00 Median : 0.0000
## Mean : 5.636 Mean : 32.77 Mean : 83.93 Mean : 0.3948
## 3rd Qu.: 8.360 3rd Qu.: 44.01 3rd Qu.:100.00 3rd Qu.: 0.0000
## Max. : 58.400 Max. :123.63 Max. :113.69 Max. :15.0000
## NA's :1024 NA's :1025 NA's :1031 NA's :739
## LIF DMT DDT DLAT
## Min. : 0.0000 Min. :0.100 Min. : 0.100 Min. :0.100
## 1st Qu.: 0.0000 1st Qu.:1.400 1st Qu.: 1.250 1st Qu.:1.000
## Median : 0.0000 Median :2.200 Median : 3.000 Median :2.400
## Mean : 0.4748 Mean :2.888 Mean : 3.152 Mean :2.625
## 3rd Qu.: 0.0000 3rd Qu.:4.485 3rd Qu.: 4.255 3rd Qu.:4.000
## Max. :15.0000 Max. :8.500 Max. :10.320 Max. :8.140
## NA's :755 NA's :1324 NA's :1324 NA's :1322
## DLIT CA CA2 MA
## Min. :0.100 Min. : 8.50 Min. :-1.120 Min. : 4.66
## 1st Qu.:1.075 1st Qu.:68.27 1st Qu.:-0.080 1st Qu.: 9.00
## Median :2.200 Median :83.22 Median : 0.010 Median :11.25
## Mean :2.432 Mean :74.87 Mean : 0.002 Mean :13.76
## 3rd Qu.:3.345 3rd Qu.:86.28 3rd Qu.: 0.100 3rd Qu.:14.00
## Max. :7.950 Max. :88.11 Max. : 0.360 Max. :60.00
## NA's :1324 NA's :1004 NA's :1026 NA's :888
## MC MB DA DC DB
## Min. : 4.70 Min. : 6.00 Min. : 4.00 Min. : 0.00 Min. : 6.0
## 1st Qu.: 9.25 1st Qu.:11.00 1st Qu.: 9.50 1st Qu.:10.24 1st Qu.:11.5
## Median :12.00 Median :13.00 Median :12.00 Median :15.00 Median :14.8
## Mean :16.09 Mean :14.25 Mean :13.89 Mean :17.79 Mean :16.5
## 3rd Qu.:19.00 3rd Qu.:16.00 3rd Qu.:15.00 3rd Qu.:21.06 3rd Qu.:18.5
## Max. :57.90 Max. :45.00 Max. :71.00 Max. :70.00 Max. :80.0
## NA's :590 NA's :1015 NA's :790 NA's :191 NA's :845
## MAVG DAVG DAVG2 TDD
## Min. : 0.00 Min. : 1.56 Min. :-0.9200 Min. : 0.20
## 1st Qu.: 9.20 1st Qu.: 7.65 1st Qu.:-0.0940 1st Qu.: 36.00
## Median :12.00 Median :11.50 Median : 0.0100 Median : 56.80
## Mean :14.44 Mean :13.69 Mean : 0.8887 Mean : 77.66
## 3rd Qu.:16.08 3rd Qu.:16.25 3rd Qu.: 0.1260 3rd Qu.:108.03
## Max. :55.00 Max. :80.00 Max. :35.0000 Max. :368.62
## NA's :646 NA's :284 NA's :1056 NA's :293
## DSDI CMA CAA CDA
## Min. : 0.6654 Min. : 16.11 Min. : 3.151 Min. : 17.79
## 1st Qu.: 0.9329 1st Qu.: 58.12 1st Qu.:23.483 1st Qu.: 80.08
## Median : 1.0000 Median : 64.83 Median :27.066 Median : 86.92
## Mean : 3.7840 Mean : 64.58 Mean :27.332 Mean : 88.09
## 3rd Qu.: 1.1157 3rd Qu.: 71.52 3rd Qu.:31.122 3rd Qu.: 94.37
## Max. :269.8500 Max. :148.96 Max. :74.262 Max. :160.74
## NA's :737 NA's :443 NA's :438 NA's :439
## MDL DDL ...61 CH
## Min. :0.0864 Min. :0.07143 Min. : NA Min. : 0.570
## 1st Qu.:0.2632 1st Qu.:0.23739 1st Qu.: NA 1st Qu.: 7.707
## Median :0.4167 Median :0.33333 Median : NA Median : 18.405
## Mean :0.4079 Mean :0.36248 Mean :NaN Mean : 28.030
## 3rd Qu.:0.5405 3rd Qu.:0.48603 3rd Qu.: NA 3rd Qu.: 40.862
## Max. :1.0638 Max. :1.11111 Max. : NA Max. :145.550
## NA's :590 NA's :191 NA's :1371 NA's :7
## TransvUndu Interdentsulci CTU1 Log_CBL
## Min. :0.0000 Min. :0.0000 Min. : 0.0 Min. :-0.9676
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 0.0 1st Qu.: 1.4545
## Median :1.0000 Median :0.0000 Median : 2.0 Median : 2.2976
## Mean :0.5149 Mean :0.4187 Mean : 1.5 Mean : 2.1924
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 3.0 3rd Qu.: 2.9848
## Max. :1.0000 Max. :1.0000 Max. :10.0 Max. : 3.9982
## NA's :969 NA's :996 NA's :969 NA's :3
## Log_CBW Log_AL Log_CBR Log_CHR
## Min. :-0.6162 Min. :-0.5978 Min. :-1.3863 Min. :-0.9163
## 1st Qu.: 0.8329 1st Qu.: 2.5177 1st Qu.:-0.7769 1st Qu.: 0.4912
## Median : 1.8083 Median : 3.3952 Median :-0.6125 Median : 0.6461
## Mean : 1.7243 Mean : 3.1984 Mean :-0.5745 Mean : 0.6339
## 3rd Qu.: 2.5975 3rd Qu.: 4.0101 3rd Qu.:-0.3718 3rd Qu.: 0.7846
## Max. : 3.8836 Max. : 5.0294 Max. : 0.7812 Max. : 1.4404
## NA's :69 NA's :338 NA's :81 NA's :14
## Log_MCL Log_MCW Log_MCR Log_MDE
## Min. :-1.139 Min. :-0.0619 Min. :-0.9570 Min. : -Inf
## 1st Qu.: 1.901 1st Qu.: 1.4996 1st Qu.:-0.6931 1st Qu.: -Inf
## Median : 2.496 Median : 2.0001 Median :-0.5543 Median : -Inf
## Mean : 2.336 Mean : 1.9078 Mean :-0.5228 Mean : -Inf
## 3rd Qu.: 2.889 3rd Qu.: 2.3943 3rd Qu.:-0.3830 3rd Qu.:2.176
## Max. : 3.614 Max. : 3.4078 Max. : 0.2462 Max. :4.067
## NA's :876 NA's :906 NA's :907 NA's :1044
## Log_MSL Log_MEC Log_LAF Log_LIF
## Min. :0.3507 Min. : -Inf Min. : -Inf Min. : -Inf
## 1st Qu.:2.8650 1st Qu.:4.230 1st Qu.: -Inf 1st Qu.: -Inf
## Median :3.3331 Median :4.605 Median : -Inf Median : -Inf
## Mean :3.2569 Mean : -Inf Mean : -Inf Mean : -Inf
## 3rd Qu.:3.7843 3rd Qu.:4.605 3rd Qu.: -Inf 3rd Qu.: -Inf
## Max. :4.8173 Max. :4.734 Max. :2.708 Max. :2.708
## NA's :1025 NA's :1031 NA's :739 NA's :755
## Log_DMT Log_DDT Log_DLAT Log_DLIT
## Min. :-2.3026 Min. :-2.3026 Min. :-2.3026 Min. :-2.3026
## 1st Qu.: 0.3365 1st Qu.: 0.2223 1st Qu.: 0.0000 1st Qu.: 0.0721
## Median : 0.7885 Median : 1.0986 Median : 0.8755 Median : 0.7885
## Mean : 0.7166 Mean : 0.7985 Mean : 0.5680 Mean : 0.5162
## 3rd Qu.: 1.5007 3rd Qu.: 1.4478 3rd Qu.: 1.3863 3rd Qu.: 1.2074
## Max. : 2.1401 Max. : 2.3341 Max. : 2.0968 Max. : 2.0732
## NA's :1324 NA's :1324 NA's :1322 NA's :1324
## Log_CA Log_CA2 Log_MA Log_MC
## Min. :2.140 Min. : -Inf Min. :1.539 Min. :1.548
## 1st Qu.:4.223 1st Qu.:-3.219 1st Qu.:2.197 1st Qu.:2.225
## Median :4.421 Median :-2.408 Median :2.420 Median :2.485
## Mean :4.272 Mean : -Inf Mean :2.498 Mean :2.629
## 3rd Qu.:4.457 3rd Qu.:-1.897 3rd Qu.:2.639 3rd Qu.:2.944
## Max. :4.479 Max. :-1.022 Max. :4.094 Max. :4.059
## NA's :1004 NA's :1182 NA's :888 NA's :590
## Log_MB Log_DA Log_DC Log_DB
## Min. :1.792 Min. :1.386 Min. : -Inf Min. :1.792
## 1st Qu.:2.398 1st Qu.:2.251 1st Qu.:2.326 1st Qu.:2.442
## Median :2.565 Median :2.485 Median :2.708 Median :2.695
## Mean :2.608 Mean :2.527 Mean : -Inf Mean :2.721
## 3rd Qu.:2.773 3rd Qu.:2.708 3rd Qu.:3.047 3rd Qu.:2.918
## Max. :3.807 Max. :4.263 Max. :4.248 Max. :4.382
## NA's :1015 NA's :790 NA's :191 NA's :845
## Log_MAVG Log_DAVG Log_DAVG2 Log_TDD
## Min. : -Inf Min. :0.4447 Min. : -Inf Min. :-1.609
## 1st Qu.:2.219 1st Qu.:2.0347 1st Qu.:-2.996 1st Qu.: 3.584
## Median :2.485 Median :2.4423 Median :-2.278 Median : 4.040
## Mean : -Inf Mean :2.3747 Mean : -Inf Mean : 4.039
## 3rd Qu.:2.778 3rd Qu.:2.7879 3rd Qu.:-1.561 3rd Qu.: 4.682
## Max. :4.007 Max. :4.3820 Max. : 3.555 Max. : 5.910
## NA's :646 NA's :284 NA's :1195 NA's :293
## Log_DSDI Log_CMA Log_CAA Log_CDA
## Min. :-0.4074 Min. :2.779 Min. :1.148 Min. :2.879
## 1st Qu.:-0.0694 1st Qu.:4.062 1st Qu.:3.156 1st Qu.:4.383
## Median : 0.0000 Median :4.172 Median :3.298 Median :4.465
## Mean : 0.1028 Mean :4.149 Mean :3.275 Mean :4.467
## 3rd Qu.: 0.1095 3rd Qu.:4.270 3rd Qu.:3.438 3rd Qu.:4.547
## Max. : 5.5979 Max. :5.004 Max. :4.308 Max. :5.080
## NA's :737 NA's :443 NA's :438 NA's :439
## Log_MDL Log_DDL Log_...61 Log_CH
## Min. :-2.4493 Min. :-2.6391 Min. : NA Min. :-0.5621
## 1st Qu.:-1.3350 1st Qu.:-1.4380 1st Qu.: NA 1st Qu.: 2.0422
## Median :-0.8755 Median :-1.0986 Median : NA Median : 2.9126
## Mean :-1.0193 Mean :-1.1308 Mean :NaN Mean : 2.8310
## 3rd Qu.:-0.6152 3rd Qu.:-0.7215 3rd Qu.: NA 3rd Qu.: 3.7102
## Max. : 0.0619 Max. : 0.1054 Max. : NA Max. : 4.9805
## NA's :590 NA's :191 NA's :1371 NA's :7
## Log_TransvUndu Log_Interdentsulci Log_CTU1
## Min. :-Inf Min. :-Inf Min. : -Inf
## 1st Qu.:-Inf 1st Qu.:-Inf 1st Qu.: -Inf
## Median : 0 Median :-Inf Median :0.6931
## Mean :-Inf Mean :-Inf Mean : -Inf
## 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.:1.0986
## Max. : 0 Max. : 0 Max. :2.3026
## NA's :969 NA's :996 NA's :9694 Data Processing
4.1 Removing columns that have more more missing value
4.2 Removing rows with NA values
4.3 Subtracting the observations for the Taxa that have least observations
lennn<-(ncol(data1_cleaned)-2)/2
taxa1<-table(data1_cleaned$CladeToothtype)
data1_cleanedd<-data.frame(taxa1)
data1_cleanedd<-data1_cleanedd[order(data1_cleanedd$Freq, decreasing = T), ]
data1_cleanedd$ID<-1:nrow(data1_cleanedd)
data1_cleanedd$CladeToothtype<-data1_cleanedd$Var1
data1_cleaned1<-data1_cleanedd[data1_cleanedd$Freq>lennn,]
data1_cleaned<-data1_cleaned[data1_cleaned$CladeToothtype%in%unique(data1_cleaned1$CladeToothtype),]
summary(data1_cleaned)## CladeToothtype Epoch
## Dromaeosauridae Lateral :295 Late Cretaceous :665
## Tyrannosauridae Lateral :175 Middle Cretaceous:166
## Troodontidae Lateral : 80 Late Jurassic :143
## Carcharodontosauridae Lateral : 74 Early Cretaceous : 60
## Abelisauridae Lateral : 63 Middle Jurassic : 41
## Non-spinosaurid Megalosauroidea Lateral: 63 Late Triassic : 36
## (Other) :369 (Other) : 8
## CBL CBW CBR CHR
## Min. : 1.38 Min. : 0.600 Min. :0.2500 Min. :0.400
## 1st Qu.: 4.89 1st Qu.: 2.300 1st Qu.:0.4529 1st Qu.:1.639
## Median :10.91 Median : 5.810 Median :0.5287 Median :1.910
## Mean :14.87 Mean : 9.181 Mean :0.5803 Mean :1.924
## 3rd Qu.:21.27 3rd Qu.:13.745 3rd Qu.:0.6659 3rd Qu.:2.185
## Max. :54.50 Max. :48.600 Max. :2.1840 Max. :3.575
##
## DC DDL CH Log_CBL
## Min. : 4.50 Min. :0.08333 Min. : 2.200 Min. :0.3221
## 1st Qu.:10.00 1st Qu.:0.25000 1st Qu.: 8.835 1st Qu.:1.5872
## Median :15.00 Median :0.33333 Median : 19.740 Median :2.3897
## Mean :17.25 Mean :0.36692 Mean : 29.830 Mean :2.3152
## 3rd Qu.:20.00 3rd Qu.:0.50000 3rd Qu.: 43.420 3rd Qu.:3.0571
## Max. :60.00 Max. :1.11111 Max. :145.550 Max. :3.9982
##
## Log_CBW Log_CBR Log_CHR Log_DC
## Min. :-0.5108 Min. :-1.3863 Min. :-0.9163 Min. :1.504
## 1st Qu.: 0.8329 1st Qu.:-0.7920 1st Qu.: 0.4942 1st Qu.:2.303
## Median : 1.7596 Median :-0.6374 Median : 0.6471 Median :2.708
## Mean : 1.7192 Mean :-0.5951 Mean : 0.6312 Mean :2.721
## 3rd Qu.: 2.6207 3rd Qu.:-0.4067 3rd Qu.: 0.7818 3rd Qu.:2.996
## Max. : 3.8836 Max. : 0.7812 Max. : 1.2740 Max. :4.094
##
## Log_DDL Log_CH
## Min. :-2.4849 Min. :0.7885
## 1st Qu.:-1.3863 1st Qu.:2.1787
## Median :-1.0986 Median :2.9826
## Mean :-1.1112 Mean :2.9528
## 3rd Qu.:-0.6931 3rd Qu.:3.7709
## Max. : 0.1054 Max. :4.9805
## 4.4 Spliting the Log variables and the original
# Select variables that contain "log" and the first column
selected_cols <- c(1,2, grep("Log", names(data1_cleaned)))
# Subset the data frame
data_log <- data1_cleaned[, selected_cols]
data_lognames(data_log)[-1] <- gsub(" ", "_", names(data_log)[-1] )
# Identify columns that contain "log"
log_cols <- grep("Log", names(data1_cleaned))
# Include the first column
cols_to_keep <- setdiff(1:ncol(data1_cleaned), log_cols)
# Ensure the first column is included
cols_to_keep <- union(1, cols_to_keep)
# Subset the data frame
data_original <- data1_cleaned[, cols_to_keep]
data_original5 Visualization
5.1 Original data
correlation_matrix <- cor(data_original[, -c(1, 2)])
# Plot correlation matrix
corrplot(correlation_matrix, method = "color", type = "lower",
addCoef.col = "black",
tl.col = "black",
tl.srt = 45,
diag = FALSE,
order = "hclust",
col = colorRampPalette(c("blue", "white", "red"))(200))
5.2 Log data
correlation_matrix <- cor(data_log[, -c(1, 2)])
# Plot correlation matrix
corrplot(correlation_matrix, method = "color", type = "lower",
addCoef.col = "black",
tl.col = "black",
tl.srt = 45,
diag = FALSE,
order = "hclust",
col = colorRampPalette(c("blue", "white", "red"))(200))
## [1] "CladeToothtype" "Epoch" "CBL" "CBW"
## [5] "CBR" "CHR" "DC" "DDL"
## [9] "CH"# Filter out taxa with less than 10 observations
data_log <- data_log %>%
group_by(CladeToothtype) %>% # Group by 'Taxa' column
filter(n() >= 10) %>% # Keep only groups with 10 or more observations
ungroup() # Ungroup after filtering
# Filter out taxa with less than 10 observations
data_original <- data_original %>%
group_by(CladeToothtype) %>% # Group by 'Taxa' column
filter(n() >= 10) %>% # Keep only groups with 10 or more observations
ungroup()
# Get the count of each unique value in the column
category_counts <- table(data_log$CladeToothtype)
# Filter unique values that have more than 0 observations
unique_values <- names(category_counts[category_counts > 0])
# Print the result
#print(unique_values)6 Merging data clade with tooth to add more classes
data1$CladeToothtype<-ifelse(!(data1$CladeToothtype%in%unique_values),data1$Clade,data1$CladeToothtype)
data1<-data1[,-c(1,2,3,4,6:14,16:19)]#until 19
#clade: 4, taxa:2, teethtaxa: 3, cladetteth: 5, epoch:15
#data<-inner_join(dd,data)
#data<-data[!duplicated(data),]
data1$`TransvUndu`<-ifelse(data1$`Transv. Undu.`!=0 & !is.na(data1$`Transv. Undu.`),1,data1$`Transv. Undu.`)
data1$`Interdentsulci`<-ifelse(data1$`Interdent. sulci`!=0 & !is.na(data1$`Interdent. sulci`),1,data1$`Interdent. sulci`)
data1$LAF<-ifelse(data1$LAF=="6-7",6.5,data1$LAF)
data1$CTU1 <- sub(".*?(\\d+).*", "\\1", data1$CTU)
data1<- data1 %>% select(-CTU,-`Interdent. sulci`,-`Transv. Undu.`)
# Convert columns to numeric, then create log-transformed columns
data1 <- data1 %>%
mutate(across(3:ncol(data1), as.numeric)) %>%
mutate(across(3:ncol(data1), log, .names = "Log_{.col}"))## Warning: There were 17 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(3:ncol(data1), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 16 remaining warnings.## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(3:ncol(data1), log, .names = "Log_{.col}")`.
## Caused by warning:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.data1$CladeToothtype<-as.factor(data1$CladeToothtype)
data1$Epoch<-ifelse(data1$Epoch=="'Middle Cretaceous'","Middle Cretaceous", data1$Epoch)
data1$Epoch<-as.factor(data1$Epoch)
#data$Taxa<-as.factor(paste0(data$`Taxa (Genus)`,data$Maturity,sep=" "))
data1<-data.frame(data1)
# Count the number of missing values in each column
missing_counts <- colSums(is.na(data1))
# Remove columns with more than 15% missing values
data1_cleaned <- data1[, missing_counts <= nrow(data1)*0.15]
# Remove rows with any NA values
data1_cleaned <- na.omit(data1_cleaned)
data1_cleanedlennn<-(ncol(data1_cleaned)-2)/2
taxa1<-table(data1_cleaned$CladeToothtype)
data1_cleanedd<-data.frame(taxa1)
data1_cleanedd<-data1_cleanedd[order(data1_cleanedd$Freq, decreasing = T), ]
data1_cleanedd$ID<-1:nrow(data1_cleanedd)
data1_cleanedd$CladeToothtype<-data1_cleanedd$Var1
data1_cleaned1<-data1_cleanedd[data1_cleanedd$Freq>lennn,]
data1_cleaned<-data1_cleaned[data1_cleaned$CladeToothtype%in%unique(data1_cleaned1$CladeToothtype),]
summary(data1_cleaned)## CladeToothtype Epoch
## Dromaeosauridae Lateral :295 Late Cretaceous :665
## Tyrannosauridae Lateral :175 Middle Cretaceous:166
## Troodontidae Lateral : 80 Late Jurassic :143
## Carcharodontosauridae Lateral : 74 Early Cretaceous : 60
## Abelisauridae Lateral : 63 Middle Jurassic : 41
## Non-spinosaurid Megalosauroidea Lateral: 63 Late Triassic : 36
## (Other) :369 (Other) : 8
## CBL CBW CBR CHR
## Min. : 1.38 Min. : 0.600 Min. :0.2500 Min. :0.400
## 1st Qu.: 4.89 1st Qu.: 2.300 1st Qu.:0.4529 1st Qu.:1.639
## Median :10.91 Median : 5.810 Median :0.5287 Median :1.910
## Mean :14.87 Mean : 9.181 Mean :0.5803 Mean :1.924
## 3rd Qu.:21.27 3rd Qu.:13.745 3rd Qu.:0.6659 3rd Qu.:2.185
## Max. :54.50 Max. :48.600 Max. :2.1840 Max. :3.575
##
## DC DDL CH Log_CBL
## Min. : 4.50 Min. :0.08333 Min. : 2.200 Min. :0.3221
## 1st Qu.:10.00 1st Qu.:0.25000 1st Qu.: 8.835 1st Qu.:1.5872
## Median :15.00 Median :0.33333 Median : 19.740 Median :2.3897
## Mean :17.25 Mean :0.36692 Mean : 29.830 Mean :2.3152
## 3rd Qu.:20.00 3rd Qu.:0.50000 3rd Qu.: 43.420 3rd Qu.:3.0571
## Max. :60.00 Max. :1.11111 Max. :145.550 Max. :3.9982
##
## Log_CBW Log_CBR Log_CHR Log_DC
## Min. :-0.5108 Min. :-1.3863 Min. :-0.9163 Min. :1.504
## 1st Qu.: 0.8329 1st Qu.:-0.7920 1st Qu.: 0.4942 1st Qu.:2.303
## Median : 1.7596 Median :-0.6374 Median : 0.6471 Median :2.708
## Mean : 1.7192 Mean :-0.5951 Mean : 0.6312 Mean :2.721
## 3rd Qu.: 2.6207 3rd Qu.:-0.4067 3rd Qu.: 0.7818 3rd Qu.:2.996
## Max. : 3.8836 Max. : 0.7812 Max. : 1.2740 Max. :4.094
##
## Log_DDL Log_CH
## Min. :-2.4849 Min. :0.7885
## 1st Qu.:-1.3863 1st Qu.:2.1787
## Median :-1.0986 Median :2.9826
## Mean :-1.1112 Mean :2.9528
## 3rd Qu.:-0.6931 3rd Qu.:3.7709
## Max. : 0.1054 Max. :4.9805
## # Select variables that contain "log" and the first column
selected_cols <- c(1,2, grep("Log", names(data1_cleaned)))
# Subset the data frame
data_log <- data1_cleaned[, selected_cols]
data_lognames(data_log)[-1] <- gsub(" ", "_", names(data_log)[-1] )
# Identify columns that contain "log"
log_cols <- grep("Log", names(data1_cleaned))
# Include the first column
cols_to_keep <- setdiff(1:ncol(data1_cleaned), log_cols)
# Ensure the first column is included
cols_to_keep <- union(1, cols_to_keep)
# Subset the data frame
data_original <- data1_cleaned[, cols_to_keep]
data_original## [1] "CladeToothtype" "Epoch" "CBL" "CBW"
## [5] "CBR" "CHR" "DC" "DDL"
## [9] "CH"# Filter out taxa with less than 10 observations
data_log <- data_log %>%
group_by(CladeToothtype) %>% # Group by 'Taxa' column
filter(n() >= 10) %>% # Keep only groups with 10 or more observations
ungroup() # Ungroup after filtering
# Filter out taxa with less than 10 observations
data_original <- data_original %>%
group_by(CladeToothtype) %>% # Group by 'Taxa' column
filter(n() >= 10) %>% # Keep only groups with 10 or more observations
ungroup()
# Get the count of each unique value in the column
category_counts <- table(data_log$CladeToothtype)
# Filter unique values that have more than 0 observations
unique_values1 <- names(category_counts[category_counts > 0])7 Saving the clean dataset
References
Hendrickx, Christophe, Octávio Mateus, and Ricardo Araújo. 2015.
“A Proposed Terminology of Theropod Teeth (Dinosauria,
Saurischia).” Journal of Vertebrate Paleontology 35 (5):
e982797. https://doi.org/10.1080/02724634.2015.982797.